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ISSN: 2586-7652 (Print) Vol. 04, No. 01, March 2021
ISSN: 2635-7607 (Online)
International Journal of Advanced Engineering
Source: http://ictaes.org
Manuscript received: December 25, 2020; Revised: January 31, 2021; Accepted: February 2, 2021
AI Applications to Combat COVID-19 Pandemic
Lisa Rajkarnikar1, Sujan Shrestha2, Surendra Shrestha3
1 Nepasoft
Solutions Private Limited, Kathmandu, Nepal
2Department of Electronics, Communication and Information Engineering, Kathmandu Engineering College, Institute of Engineering,
Nepal,
3Department of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Nepal
lisarajkarnikar@gmail.com1, shrestha.sujan1400@gmail.com2, surendra@ioe.edu.np3
Abstract
The novel outbreak of corona virus (COVID-19 or SARS-COV-2) is spreading worldwide increasingly. Soaring
COVID-19 positive cases culminated in an urgent need for pandemic monitoring and supervision. In these
scenarios, the implementation of Artificial Intelligence techniques can potentially provide analytical and decision-
making assistance. We plan to examine the essential role of AI in the diagnosis, detection, recovery and response
of the COVID-19 pandemic. We reviewed a variety of research work on how and when to implement AI to counter
the COVID-19 pandemic. Assessing the efficacy of AI for COVID-19, we have segregated its application toward
Novel COVID-19 into seven different aspects. We pivoted mainly on the Abstract, Methodology, and Conclusion
of the particular model and looked at the plausibility of how and where it fits best to combat the novel Corona
virus.
Keywords: Corona virus, COVID-19, SARS, MERS, Deep Learning
1. Introduction
About 96 million people have been affected globally by novel Corona virus with death tolls of about 2.08 million.
The Chinese authorities reported SARS-like and MERS-like viruses in their communities (Wuhan, China) in
December 2019. The World Health Organization proclaimed the outbreak of the virus to be an international public
health emergency in January 2020 and a pandemic in March 2020 [1].
Artificial intelligence (AI) refers to a computer science field dedicated to the creation of systems that perform
tasks that generally require human intelligence [2]. The applications of AI extend from education, recreation,
robotics, and agriculture to health sectors, finance and what not. Various AI techniques can be implemented to
assist medical industry in quickly controlling and maintaining COVID-19 pandemic. With the help of these, policy-
Corresponding Author : Surendra Shrestha
Author’s affiliation : Dept.of Electronics and Computer Engineering, Pulchowk Campus, Institute of Engineering, Nepal
Email: surendra@ioe.edu.np
Copyright © ICT-AES2 International Journal of Advanced Engineering, Vol.04, No.01, pp.1-8
makers, government and health professionals can better understand the COVID-19 virus and implement their action
according to it. AI technology can improve planning, controlling and monitoring of the global COVID-19
pandemics and also learn and build the system to predict upcoming pandemics in future.
2. Principal Applications of AI for Novel COVID-19 Pandemic
AI tools can be implemented for analysis, screening, prediction, monitoring and prevention of COVID-19
pandemic. The principal application of AI at different stages of COVID-19 can be summarized into seven different
sections as shown in Figure 1.
Figure 1. Application of AI at different stages of COVID-19 pandemic
2.1. Early detection and diagnosis
AI can assist to the timely diagnosis of COVID-19 by examining irregular patterns of symptoms and alert them
to take precautionary measures. Using suitable AI algorithm implementing imaging technologies such as CT scan,
X-ray images, MRI scan and audio technologies such as Breathing and coughing voice, it can assist infected people
to diagnose the new disease. A binary classifier is used in [3] to distinguish the cough sound of COVID-19 positive
patient with a healthy person’s cough sound and also with the cough sound of Asthma infected person. Similarly,
a system of GRU neural network implemented with bidirectional and attentional mechanisms can classify 6
clinically significant respiratory patterns hence identifying patients with COVID-19 infection [4].
We can contribute to the high-speed, high-precision diagnosis of the disease compared to traditional methods
using AI techniques as shown in Table 1.AI Applications to Combat COVID-19 Pandemic 3
Table 1. Research Paper related to application of AI in early detection and diagnosis of COVID-19
Papers AI Technology Used Description Performance Metrics Dataset used
[5] CNN with VGG16 Classifies COVID-19 Sensitivity:93.28%, [6],[7],[8]
architecture and cases from X-ray images. Specificity:94.61%,
synthetic data Precision :94.90%,
augmentation Accuracy: 94.88% F-
technique score: 93.10%
[9] Recurrent Neural Performs early diagnosis Precision, Recall,F1- Voice, cough, breathing
Network, Long Short of COVID-19 evaluating score,AUC, Accuracy sounds from different
Term Memory different acoustic features. United Arab Emirates
hospitals
[10] Random forest Accurately identify Accuracy:95.95% Blood samples collected
Algorithm COVID-19 using blood Specificity: 96.95% from different hospitals
test sample of Lanzhou and Gansu
2.2. Prevent the spread of disease
Various machine learning algorithms can be used to determine and forecast the location of the next outbreak
based on the use of travel, payment and communication data. This study may suggest that policy makers and
governments should take appropriate steps to prevent the spread of disease. Advanced deep learning algorithm has
been paired with geometric strategies for secure social distancing and face mask detection in public areas [11].
Comparison of various pre-defined deep learning models can be implemented for Face mask detection system as
shown in Table 2.
Furthermore, Haar Wavelet Transform and Local Binary Pattern can be implemented to measure the
temperature of an individual without physical touch [12]. Since fever is a common symptom in patients with
COVID-19, the Human Face Thermal Recognition can play important role in the pandemic.
Usage of Face mask Detection and Thermal recognition application in the entrance gate of offices, schools, and
banks can help to prevent the spread of disease in a large context. Similarly, AI techniques can analyze and predict
the future need of beds and medical equipments to fight against the infection. Also, it can suggest policy makers
and government for the need of lockdown and border checks to track the disease in real-time.
Table 2. Comparison of Accuracy between different models for Face Mask Detection System
Papers Architecture Used Introduced Year Accuracy (%)
[13] LeNet – 5 1998 84.6
[14] AlexNet 2012 89.2
[15] SSDMNV2 (single shot multibox 2020 92.64
detector and MobileNetV2)
2.3. Contact Tracing
Several countries have introduced contact tracking applications based on Mobile history, Bluetooth, contact
information, network-based API, mobile tracking data, card transaction data, and various other media. Amid the
COVID-19 outbreak, digital contact tracing application used by different countries has helped to combat the virus
as shown in Table 3 [16]. The implementation of the AI algorithm to analyze data obtained from these sources can
accelerate the task of contact tracing and therefore contribute to the rapid flattening of the COVID-19 curve. In4 International Journal of Advanced Engineering, Vol.04, No.01, pp.1-8
[17], a solution framework has been proposed to prevent and monitor the COVID-19 pandemic involving effective
contact tracing in smart cities
Table 3. Worldwide implementation of digital health technology for COVID-19 contact tracing
Countries Description Technology Feature Reference
Stopp Corona app notifies users of potential
Austria Bluetooth technology [18]
exposure.
COVIDSafe app notes the date, time, distance and
Australia duration of contact with other users and notifies Bluetooth technology [19]
users of potential exposure.
Interfaces with other
Close contact detector app provides users with widely-used apps such
China [20]
unique QR codes. as WeChat, Alipay and
QQ. blood test sample
Corona-Warn app scans identification codes on
Germany nearby phones and notifies user upon exposure to Bluetooth technology [21]
proximal code.
GH Covid-19 Tracker app provides detailed
Bluetooth and GPS
Ghana information on event, location after potential [22]
technology
exposure.
Singapore TraceTogether app Bluetooth technology [23]
Switzerland SwissCovid app previous information about users Bluetooth technology [24]
in close contact. Cantonal authorities notify other
users of exposure.
2.4. Analyze and Monitor for proper treatment
AI can provide proper treatment for the new disease in quick time as number of infected people is increasing
rapidly. The AI algorithm can intelligently analyze patient health data, imaging data, demographic data, lifestyle,
and other data to provide economical and personalized treatment to improve the traditional symptom-driven and
generalized treatment [25].
Since the convalescent plasma from the body of COVID-19 recovered patients can be used to boost the
immunity of the COVID-19 positive patients, machine learning algorithms can be implemented for the best
selection of convalescent plasma to transfuse to the critical Covid-19 patients [26]. It can provide quick and
effective decision-making in medical sector.
2.5. Drugs and vaccine Discovery
Owing to the large rise in the number of COVID-19 infected people, there is an imminent need for medicines
and vaccines for new infections. The task of drugs and vaccine discovery can be accelerated if the behavior of
virus be well known. Machine learning algorithm namely SVM, Linear Regression and KNN are used to find
the sequence of protein in the virus [27]. Similarly, time series analysis can be done using Recurrent Neural
Network (RNN) and LSTM model to predict the mutation rate of virus [28]. It analyzes the nucleotide and codon
mutation separately [29].AI Applications to Combat COVID-19 Pandemic 5
AI can examine the symptoms and available medication data and recommend effective medicines in order
to accelerate medical research. It can become a helpful tool for the development of vaccines and test designs for
diagnosis.
2.6. Support Healthcare organization
There is an instant need for a decision-making framework for health professionals to set priority for the care of
patients due to the large rise in COVID-19 positive cases. We can process the clinical, radiological, and laboratory
data of COVID19-related patients in order to predict the mortality rate with different machine learning algorithms
such as Boruta, Random Forest, and Associative trees.
Various machine learning techniques like random forest (RF) and artificial neural network (ANN) methods with
LC-ARIMA model can prediction of mortality rates [30]. This may assist the healthcare organization to triage the
patients by analyzing their mortality risk [31].Similarly, the system implementing three machine learning
algorithms namely SVM, Regression model and ANN survey and predict patient’s health condition. It can act as
great decision making tool and helping hand for health professionals [32]. AI can predict the future possibility of
cases and alarm the healthcare organization to be ready for it. Furthermore, the application of AI can accelerate
training and education for healthcare employees.
2.7. Assist response to crisis and recovery to follow
Covid-19's soaring cases are hitting around the globe and there has been fake COVID-19 information on social
media and social networking sites. By implementing the sentimental analysis, the fake content can be identified
and removed. Decision Tree can be used to classify and filter out fake COVID-19 related news [29]. It implements
the dataset of total 399 news which consists of 299 fake news and extracts features from the news headlines,
Linguistic Inquiry and Word Count Engine [33]. For fake news detection, a deep diffusive unit model can work as
helpful tool which accepts multiple inputs from different sources fuse them and generates output [34]. Table 4
shows comparison of different classifiers for Fake News Detection based on its accuracy.
In order to help people recognize COVID-19 symptoms and prescribe punitive measures, many hospitals and
clinics have provided automated AI assistants and chat bots. Furthermore, from the current COVID-19 pandemic
data, various deep learning algorithms can learn and develop the framework to report potential outbreaks.
Table 4. Comparison of different Classifier for Fake News Detection
Papers Classifier Accuracy (%) Dataset Used
[35] Support Vector Machine 89% Ott et al. reviews dataset
[36] Lagrangian Support Vector Machine 90% Ott et al. reviews dataset
[37] Logistic regression 78% Reviews Amazon website
3. Conclusion
In order to counter the devastating virus that impacts worldwide, the healthcare sector needs the assistance of
advanced machine learning, IOT, big data, and AI services. Through monitoring, controlling, and continuously
participating in the production of vaccines, we can tackle the ongoing pandemic with the combined implementation
of AI and health professionals. In a nutshell, AI can be quite useful for monitoring and preventing the spread of
virus and the outbreak of COVID-19 can be completely eradicated by more and more research in this field.6 International Journal of Advanced Engineering, Vol.04, No.01, pp.1-8
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